Development of an HPLC–UV method for quantification of posaconazole in low-volume plasma samples: design of experiments and machine learning models

Abstract

Abstract Posaconazole (PCZ) is a triazole antifungal agent with a broad-spectrum activity. Our research aims to present a novel approach by combining a 2-level fractional factorial design and machine learning to optimize both chromatography and extraction experiments, allowing for the development of a rapid method with a low limit of quantification (LOQ) in low-volume plasma samples. The PCZ retention time at the optimized condition (organic phase 58%, methanol 6%, mobile pH = 7, column temperature: 39 °C, and flow rate of 1.2 mL/min) was found to be 8.2 ± 0.2 min, and the recovery of the PCZ at the optimized extraction condition (500 µL extraction solvent, NaCl 10% w/v, plasma pH = 11, extraction time = 10 min, and centrifuge time = 1 min) was calculated above 98%. The results of machine learning models were in line with the results of experimental design. Method validation was performed according to ICH guideline. The method was linear in the range of 50–2000 ng/mL and LOQ was found to be 50 ng/mL. Additionally, the validated method was applied to analyze PCZ nanomicelles and conduct pharmacokinetic studies on rats. Half-life (t1/2), mean residence time (MRT), and the area under the drug concentration–time curve (AUC) were found to be 7.1 ± 0.6 h, 10.5 ± 0.9 h, and 1725.7 ± 44.1 ng × h/mL, respectively. Graphical Abstract

Keywords

• Posaconazole
• HPLC
• Chromatographic condition
• Sample preparation condition
• Design of experiments
• Machine learning